Solving a real-world rail maintenance scheduling problem

Abstract

The rail network in the Netherlands is one of the busiest in Europe. To ensure a safe and reliable infrastructure, preventive maintenance is of utmost importance. ProRail, the sole maintainer of the railway infrastructure in the Netherlands, spends hundreds of millions of dollars on maintenance each year. Due to the complexity and busyness of the network, maintenance can cause major disruptions leading to longer travel times. Despite these factors, maintenance is currently scheduled mostly manually, leading to suboptimal schedules being created. A pilot study by Macomi, the company which enabled this study through an internship, showed great potential for improvement in scheduling this maintenance. In this thesis, the aim is to further improve the maintenance schedule of ProRail, as well as to find out what kind of solution method is most suitable to improve that schedule. To achieve this goal, various types of algorithms have been implemented and tested on the problem. An evolution strategy algorithm developed by Macomi was successfully improved. Through the use of multi-objective algorithms, the trade-off between maintenance costs and availability of the infrastructure was analyzed. These multi-objective algorithms were found to provide unsatisfactory solutions. Greedy algorithms were also developed to provide a quicker solution method, and the resulting solutions were of surprisingly high quality. Finally, a hybrid algorithm using the evolution strategy and a greedy algorithm was created. It was shown that this hybrid algorithm outperforms all other algorithms, and provides solutions that are better in terms of costs and constraints compared to the manual schedule of ProRail, and the baseline established by the pilot study of Macomi.